At 1X2.TV, we employ multiple neural network architectures within our prediction ensemble, each contributing different analytical strengths. This article provides a technical overview of the deep learning approaches that power our football predictions and explains why different architectures excel at different aspects of match forecasting.
Feed-Forward Networks for Feature-Based Prediction
Our baseline neural network is a feed-forward architecture with multiple hidden layers that processes tabular feature inputs — team ratings, form indices, head-to-head records, and contextual factors. We use batch normalization and dropout regularization to prevent overfitting to our training data, which spans multiple seasons across all supported leagues. The feed-forward network excels at capturing non-linear relationships between input features and match outcomes that simpler models like logistic regression might miss.
Recurrent Networks for Form Sequences
Team form is inherently sequential — the order of results matters, not just their aggregate. We employ LSTM (Long Short-Term Memory) networks to process sequences of match results, capturing momentum patterns and form trajectories that feed-forward networks cannot model effectively. The LSTM component receives a sequence of the last 10-15 matches for each team, encoding features like goals scored, goals conceded, xG, possession, and shots on target for each match in the sequence.
Attention Mechanisms for Key Match Features
We've integrated attention mechanisms that allow our models to dynamically weight the importance of different input features based on the specific match context. For example, in a derby match, the attention mechanism may upweight head-to-head historical data, while in a match between teams from different divisions (cup competitions), it may prioritize league position and squad quality metrics.
Ensemble Integration
No single neural network architecture consistently outperforms all others across all prediction markets. Our final predictions are generated by an ensemble that combines outputs from feed-forward, LSTM, and attention-based models with gradient-boosted decision trees and Elo-based systems. The ensemble weights are themselves learned through a meta-learning process that optimizes overall prediction accuracy.
Continuous Learning
Our neural networks are retrained weekly with new match data, ensuring they adapt to evolving team dynamics. We use online learning techniques to update model weights incrementally without full retraining, allowing rapid adaptation to mid-season changes like manager appointments or key transfers.